Confusion Matrix
A confusion matrix, typically represented as a table, is a popular evaluation metric used to describe the performance of a classification model (or “classifier”). The table compares predicted and actual values. The basic components of the table are as follows:
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True positives (TP): The prediction was yes, and the true value is yes
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True negatives (TN): The prediction was no, and the true value is no
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False positives (FP): The prediction was yes, but the true value was no
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False negatives (FN): The prediction was no, but the the true value is yes
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Related Metrics
The confusion matrix is closely related to other metrics like Precision, Recall/Sensitivity, Specificity, and F1 Score. Those definitions are as follows:
| Metric | Formula | Definition |
| Accuracy | (TP+TN)/(TP+TN+FP+FN) | Percentage of total items classified correctly |
| Precision | TP/(TP+FP) | How accurate the positive predictions are |
| Recall/Sensitivity | TP/(TP+FN) | True positive rate (eg to asses false positive rate) |
| Specificity | TN/(TN+FP) | True negative rate (eg to assess false negative rate) |
| F1 score | 2TP/(2TP+FP+FN) | A weighted average of precision and recall |